The global geography of artificial intelligence in life science research

Artificial intelligence (AI) promises to transform medicine, but the geographic concentration of AI expertize may hinder its equitable application. We analyze 397,967 AI life science research publications from 2000 to 2022 and 14.5 million associated citations, creating a global atlas that distinguishes productivity (i.e., publications), quality-adjusted productivity (i.e., publications stratified by field-normalized rankings of publishing outlets), and relevance (i.e., citations). While Asia leads in total publications, Northern America and Europe contribute most of the AI research appearing in high-ranking outlets, generating up to 50% more citations than other regions. At the global level, international collaborations produce more impactful research, but have stagnated relative to national research efforts. Our findings suggest that greater integration of global expertize could help AI deliver on its promise and contribute to better global health.


Table of Contents
-The paper investigates funding by the U.S. National Science Foundation (NSF) and the National Natural Science Foundation of China (NSFC) from 2010 to 2019 in the field of artificial intelligence (AI).
-It compares the amount of funding and number of AI-related awards granted by the NSF and NSFC during the specified period.
-It identifies the key institutions and universities that received AI awards from the NSF and NSFC.
-The NSF granted a higher amount of funding and number of AI-related awards than the NSFC in the period of 2010-2019, the former granted approximately 2 billion in AI-related awards through 5,454 projects during this period.
-The paper analyses the inter-city collaborations and talent migrations in the field of AI using a dataset of 2. -The paper assesses the performance of the innovation system and identifies systemblocking mechanisms for AI healthcare technology innovations in the life science industry.
-It uses the Technological Innovation Systems (TIS) framework to analyze the structural and functional dynamics of AI healthcare technology innovations in West Sweden.
-It employs a mixed-method research approach, combining qualitative and quantitative data from secondary published sources and interviews with experts and life science business executives.
-The results highlight that limited resources and insufficient communication from healthcare professionals regarding their needs for improving healthcare using AI technology innovations are the main system weaknesses restricting innovation system performance.
-The study suggests that policy interventions to increase available resources and formulate vision and mission statements to improve healthcare with AI technology innovations may enhance innovation system performance.Bloom, N., Hassan, T. A., Kalyani, A., Lerner, J., & Tahoun, A. (2021).The diffusion of disruptive technologies (No. w28999).National Bureau of Economic Research.
-The paper utilizes the full text of millions of patents, job postings, and earnings conference calls to study the development and diffusion of disruptive technologies across various dimensions.
-The authors establish five stylized facts about the development and diffusion of disruptive technologies, including their spread across space, skill levels, and other dimensions.
-The analysis identifies 29 disruptive technologies -Artificial Intelligence is one of themthat had significant implications for businesses and jobs in the United States over the past two decades.
-The study shows that disruptive technologies typically emerge from a handful of urban areas, which house the majority of early patenting and employment in the technology before its commercial breakthrough.
-The paper identifies and categorizes various AI applications against COVID-19, including diagnosis and screening, drug discovery, epidemiology and public health, and social control and monitoring.
-AI has shown promise in accelerating the development of diagnostics, therapeutics, and vaccines for COVID-19.
-The paper discusses the challenges and limitations of AI applications in the context of COVID-19, such as data privacy concerns and the need for robust validation and regulation.
-It emphasizes the importance of interdisciplinary collaborations and the integration of AI with other technologies in the fight against COVID-19.
-The paper provides insights into the future directions and opportunities for AI in combating the pandemic, including the potential for AI to assist in early detection and prediction of outbreaks.Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2020).Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions.Sustainability, 12 (2), 492.
-The paper aims to analyze the scientific literature on the application of artificial intelligence (AI) and machine learning (ML) in the manufacturing industry, specifically in the context of smart production.
-It conducted a literature review on ML and AI empirical studies published from 1999 to the present, highlighting the evolution of the topic before and after the introduction of Industry 4.0.
-The analysis included classification of the literature based on publication year, authors, scientific sector, country, institution, and keywords.
-The review identified 82 articles, with a greater number of works published by the USA and an increasing interest in AI and ML after the birth of Industry 4.0.Claude, R., Charles-Daniel, A., Jean, A., & Jean-Francois, G. (2004).Bibliometric overview of the utilization of artificial neural networks in medicine and biology.Scientometrics, 59, 117-130.
-The study provides an analysis of articles involving artificial neural networks (ANN) in medicine and biology, considering parameters such as the number of articles, impact factor, journal category, source country population, and gross domestic product.
-The top five countries with the most publications on artificial neural networks (ANN) in medicine and biology in 2004 were the USA, United Kingdom, Germany, Italy, and Canada.Other active countries in ANN research in these fields included Sweden, Netherlands, Spain, France, Japan, and China.
-The paper presents the distribution of ANN publications among subdisciplines of life sciences and clinical medicine.
-It highlights the impact of recent developments in biology, biotechnologies, and the growing research on ANN in biomedical sciences.Davenport, T., & Kalakota, R. (2019).The potential for artificial intelligence in healthcare.Future healthcare journal, 6(2), 94.
-The paper discusses the different types of AI relevant to healthcare and the specific processes and tasks they support.
-AI can perform healthcare tasks as well or better than humans, such as diagnosing diseases and guiding researchers in clinical trials.
-Implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period.
-Ethical issues related to the application of AI in healthcare are also discussed in the paper.Guo, Y., Hao, Z., Zhao, S., Gong, J., & Yang, F. (2020).Artificial intelligence in health care: bibliometric analysis.-The paper provides a comprehensive analysis of the worldwide adoption of AI technology across different fields of research from 1960 to 2021, using bibliometric analysis with 137 million peer-reviewed publications captured in the Lens.orgdatabase.
-It defines AI using a list of 214 phrases developed by expert working groups at the Organisation for Economic Cooperation and Development (OECD).
-The research findings reveal a surge in AI adoption across practically all research fields, including physical science, natural science, life science, social science, and the arts and humanities, in recent years.
-The diffusion of AI beyond computer science was early, rapid, and widespread, with over half of all research fields being related to AI by 1972 and over 98% in current times.
-The paper notes that the current surge in AI adoption appears different from previous boom-bust cycles, suggesting that interdisciplinary AI application is likely to be sustained.Klinger, J., Mateos-Garcia, J., & Stathoulopoulos, K. (2018).Deep learning, deep change?Mapping the development of the Artificial Intelligence General Purpose Technology.arXiv preprint arXiv:1808.06355.
-The paper analyses Deep Learning (DL) as a General Purpose Technology (GPT) and its rapid growth, diffusion into new fields, and impact in those fields, describing the changes in the geography of DL, including China's rise in AI rankings and the relative decline of several European countries.
-It identifies the consolidation of DL research hubs, suggesting a closing window of opportunity for new entrants.
-It studies of the regional drivers of DL clustering, highlighting the importance of proximity between GPT developers and adopters for collaboration and knowledge sharing.
-Discovery of a Chinese comparative advantage in DL after controlling for other factors, emphasizing the significance of data access and supportive policies for successful DL development.
-The paper discusses how AI is revolutionizing the life sciences sector, particularly in biomedicine and healthcare, by improving disease diagnosis and patient outcomes, as well as reducing healthcare costs.
-It highlights the increasing use of AI in clinical trials, where patient information can be collected in real-time and explored using various AI tools.
-It mentions the use of mobile technologies associated with AI to improve aspects of disease diagnosis and treatment, offering patient-centric solutions.
-AI is shown to have applications in drug development and repurposing, enabling faster diagnosis, more efficient treatment, and the identification of data-driven hypotheses for scientists.
-The authors discuss the potential of big data analytics in the life sciences, emphasizing the importance of computational resources to handle the increasing volume and complexity of data.Lundvall, B. Å., & Rikap, C. (2022).China's catching-up in artificial intelligence seen as a coevolution of corporate and national innovation systems.Research Policy, 51 (1), 104395.
-The paper explores China's emergence as a lead country in artificial intelligence, highlighting the co-evolution of corporate and national innovation systems (NIS).
-It introduces the concept of "corporate innovation system" (CIS) and emphasizes the increasing importance of big companies as network leaders.
-It discusses the interaction within China's national innovation system and its openness, using Japan as a reference.
-It focuses on two Chinese tech giants, Alibaba and Tencent, and their innovation activities, which rely on knowledge sources within China's NIS and privileged access to Chinese data, also highlighting their international activities.Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., & Abedi, V. (2019).Artificial intelligence transforms the future of health care.The American journal of medicine, 132(7), 795-801.
-The study looks at Artificial Intelligence (AI) in healthcare and its potential to unlock novel insights and accelerate breakthroughs by analyzing large, integrated datasets.
-Machine learning applications in healthcare have been used in clinical, translational, and public health settings, with a focus on privacy, data-sharing, and genetic information.
-Unsupervised learning, which aims to identify hidden patterns in data, has been used to explore data and generate novel hypotheses in various fields, including infectious disease distribution and heart failure research.
-Regularized logistic regression is an important tool for analyzing large-scale genotype or phenotype data, and sharing data across institutions is crucial for the success of genetic and biomedical studies.
-The development of complex algorithms in precision medicine offers compelling opportunities but also computational challenges, such as handling large volumes of data and integrating different data formats.Radanliev, P., De Roure, D., Maple, C., & Santos, O. (2022).Forecasts on future evolution of artificial intelligence and intelligent systems.IEEE Access, 10, 45280-45288.
-The study conducts a statistical analysis of research data records on artificial intelligence by year, country, language, and organization.
-It finds that the USA is the leading country in the field of artificial intelligence on a national level.
-Identifying English as the dominant language for disseminating results in the field of artificial intelligence.-The main focus areas of AI in healthcare include health services management, predictive medicine, patient data and diagnostics, and clinical decision-making.
-The United States, China, and the United Kingdom contributed the highest number of studies in this field.
-AI has several applications in health services, such as supporting physicians in making diagnoses, predicting disease spread, and customizing treatment paths.
-The analysis of journals in this field confirms that AI in healthcare is an interdisciplinary research field, with contributions from medical journals or journals focused on technological growth in healthcare.Simon, J. P. (2019).Artificial intelligence: scope, players, markets and geography.Digital Policy, Regulation and Governance, 21(3), 208-237.
-The paper provides a comprehensive overview of the major trends in the field of artificial intelligence (AI).
-It identifies pioneering companies and the geographical distribution of AI companies, The USA dominates investment in AI, with significant growth and size in the region.Asia, North America, and Europe also show growth in investment, number of AI companies, and number of patents in the AI field.Europe has start-up hubs in Paris, London, and Berlin that focus intensively on AI.
-It notes the lack of consensus on a definition for the umbrella term of AI, and it highlights changes and advances in the past 60 years, together with the uncertainty in the demand for AI and the challenges in assessing the scope of disruptions and technological innovation associated with AI.
-The paper acknowledges the limitations of available research on economic and social aspects of AI, as most of the data come from consultancies or government publications, which may introduce some bias.-Concentration of AI patenting activities in the sectors of software programming and manufacturing of electronic equipment and machinery, and signs of cross-fertilization towards non-tech sectors.
-Mainland China leads in AI patenting activities, filing more patent families than Japan, its closest follower.The performance of China is particularly striking, with a significant increase in AI patents in recent years.The EU has a lower average number of patent families compared to China, South Korea, and the US.
-The US and South Korea have a comparative advantage in information and communication sectors, as well as manufacturing of electronic equipment.China and Japan have relative strength in manufacturing of electronic equipment and machinery.Wolff, J., Pauling, J., Keck, A., & Baumbach, J. (2020).The economic impact of artificial intelligence in health care: systematic review.Journal of medical Internet research, 22(2), e16866.
-The paper systematically reviews and summarizes cost-effectiveness studies dedicated to AI in healthcare.
-It identifies methodological deficits in existing economic impact assessments of AI in healthcare.
-It emphasizes the need for more comprehensive economic analyses in future studies to enable economic decisions regarding the implementation of AI technology in healthcare.
-It highlights the importance of considering initial investment and operational costs for AI infrastructure and services in economic impact assessments.
-It also suggests evaluating alternatives to achieve similar impact to provide a comprehensive comparison in economic impact assessments of AI in healthcare.Xin, Y., Man, W., & Yi, Z. (2021).The development trend of artificial intelligence in medical: A patentometric analysis.Artificial Intelligence in the Life Sciences, 1, 100006.
-The study uses Social Network Analysis (SNA) to characterize patent applications and cooperative networks in the AI-medical field.
-It maps a holistic landscape related to the AI-medical field using the Derwent Innovation Index database as the patent data source, identifying the United States as the foremost country developing related technologies and the primary target of patent filing by nonresidents.
-It identifies hotspots in the AI-medical field, including medical image recognition, computer-aided diagnosis, disease monitoring, disease prediction, bioinformatics, and drug development.
-It finds that companies and academic institutions are the most active innovation subjects in the AI-medical field, with domestic collaboration being the major collaborative pattern.Xu, D., Liu, B., Wang, J., & Zhang, Z. (2022).Bibliometric analysis of artificial intelligence for biotechnology and applied microbiology: Exploring research hotspots and frontiers.Frontiers in Bioengineering and Biotechnology, 10, 998298.
-The paper provided a bibliometric framework to track biotechnological developments and explore specific knowledge areas.
-It analyzed 3,529 scientific papers on AI applications in biotechnology and applied microbiology published between 2000 and 2021.The United States had the highest number of publications, and 128 countries contributed to the research in this field.
-A total of 584 global institutions were involved in publishing these papers, with the Chinese Academy of Science being the most prolific.

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-The reference clusters from the studies were categorized into ten main headings, including deep learning, prediction, support vector machines (SVM), object detection, feature representation, synthetic biology, amyloid, human microRNA precursors, systems biology, and single-cell RNA-Sequencing.
-The report offers insights into the effects of COVID-19 on AI development, including the use of machine-learning-based techniques for COVID-related drug discovery and the impact on AI hiring and private investment.
-The shift to virtual formats for AI research conferences due to the pandemic may have led to increased participation, as evidenced by significant spikes in attendance.
-The report also includes information on jobs in AI, datasets, and computational experiments, offering practical resources for researchers and practitioners in the field.

S2 Number of AI-Related Life Science Articles by Country (2000-2022)
Lead

S3 Explanation of Concepts recorded in OpenAlex
The OpenAlex database tags articles with multiple concepts representing their topical focus using a state-of-the art machine learning classifier based on titles and abstracts, with confidence scores indicating relevance. 1These scientific concepts are organized hierarchically, with 19 root-level concepts branching into six levels of specific topics.When a lower-level concept is mapped, all of its parent concepts are mapped as well, ensuring comprehensive coverage.This structure supports a rich network of interconnected scientific entities, facilitating advanced querying and analysis. 2After performing a review of pertinent

S6 Sample Creation and Accuracy
To evaluate the accuracy with which the applied search approach allows us to identify AI focused life science research, we assess precision and comprehensiveness.First, we analyze precision, which is the extent to which the articles we retrieve are relevant, i.e., actually contain AI focused life science research.Second, we analyze comprehensiveness, which is the extent to which all relevant articles are retrieved with our search approach.

Precision
To evaluate precision, we took a random sample of 150 articles from our sample of PubMed publications and hired two independent raters to categorize the documents into having an AI application.Additionally, we took a random sample of 150 conference proceedings publications and asked the same two raters to categorize the documents as being linked to the life sciences.
Across all 300 manually checked documents, the observed agreement among raters was 96%.
Out of the 150 PubMed publications, 135 (90%) were categorized as having some form of AI applications.Out of the 150 conference proceedings publications, 139 (93%) were categorized as being related to the life sciences.The AI relevance of conference proceedings publications and the life science relevance of PubMed publications is given by construction of our dataset.

Comprehensiveness
To assess comprehensiveness, we identified special issues on Artificial Intelligence published by journals indexed in PubMed.Specifically, we searched for editorials published in 2022 that contained the keywords "special issue" and "artificial intelligence," yielding 15 special issues with a total of 184 articles.In total, 170 out of 184 papers (92%) were included in our dataset.
Upon manual inspection of the 14 non-recalled articles, we find that five non-recalled articles contain AI-identifying keywords in the main text but not in the title and abstract, while two nonrecalled articles use more general terms such as "algorithm", thereby evading our applied keyword identification.The remaining three articles are commentaries without a dedicated abstract, which reduces the likelihood of keyword-based recalls from titles alone.There is some variance in recall by special issue (with a minimum of about 79% at the issue level).
Table S6 provides an overview of the special issues and the corresponding proportion of articles covered in our sample.
S6. Special issues and publications included in our sample.
We also compared the search approach with a second search strategy presented by Liu and colleagues. 4We find that the search approach by Liu and colleagues, which relies on a smaller set of keywords to identify AI-related research, would yield a sample of approximately 180,000 AI-related life science publications in PubMed.Of these 180,000 articles, 93% are included in our sample obtained by applying the search strategy proposed by Baruffaldi and colleagues.
In addition, a manual review of a random set of 100 PubMed articles identified by the sciences (S7.6).In terms of relevance of the research produced, the world regions of Northern America and Oceania generate a consistent citation premium over other world regions for their AI life science research (S7.7).European research, on the other hand, generates a citation premium only for journal publications.The higher productivity of Asian countries for conference proceedings publications is not associated with a citation premium.Finally, international collaborations seem to be less frequent for conference proceedings publications than for journal publications.
Irrespective of the research field, international collaborations generate a citation premium compared to national collaborations, but have stagnated (S7.8 -S7.9).

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It points the confusion regarding the leading organization in the field of artificial intelligence, with conflicting results between the Chinese Academy of Sciences and the University of California.Schwalbe, N., & Wahl, B. (2020).Artificial intelligence and the future of global health.The Lancet, 395(10236), 1579-1586.-The paper discusses the potential of artificial intelligence (AI) in addressing unique challenges in global health and accelerating the achievement of health-related sustainable development goals in low and middle-income countries (LMICs).-It highlights the deployment of AI in LMICs for various health issues, particularly communicable diseases like tuberculosis and malaria, using machine learning and signal processing methods.-It categorizes AI-driven health interventions into four areas: diagnosis, patient morbidity or mortality risk assessment, disease outbreak prediction and surveillance, and health policy and planning.-It emphasizes the need for ethical, regulatory, and practical considerations in the development, testing, and widespread use of AI-driven interventions in global health.-It suggests that despite being a nascent field, AI-driven health interventions have the potential to improve health outcomes in LMICs, but guidelines and a user-driven research agenda are necessary for equitable and ethical use.Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021).The role -The study analyses 288 peer-reviewed papers from Scopus, highlighting that the literature on AI in healthcare is emerging.of artificial intelligence in healthcare: a structured literature review.BMC medical informatics and decision making, 21, 1-23.
Tran, B. X., Vu, G. T., Ha, G. H., Vuong, Q. H., Ho, M. T., Vuong, T. T., ... & Ho, R. C. (2019).Global evolution of research in artificial intelligence in health and medicine: a bibliometric study.Journal of clinical medicine, 8(3), 360.-The study provides a global and historical overview of research on Artificial Intelligence (AI) in health and medicine, analyzing the publication volume, authors, and countries collaboration in the field of AI in health and medicine.-It identifies major techniques used in AI research, including Robotic, Machine learning, Artificial neural network, Artificial intelligence, and Natural language process.-It highlights the most frequent applications of AI in Clinical Prediction and Treatment.-It identifies the highest number of cancer-related publications, followed by Heart Diseases and Stroke, Vision impairment, Alzheimer's, and Depression.-It suggests future directions in AI research by pointing out the shortage of research on AI application to some high burden diseases and recommends the development of global and national protocols and regulations for the justification and adaptation of medical AI products.Van Roy, V., Vertesy, D., & Damioli, G. (2019).AI and robotics innovation: A sectoral and -The study finds a tremendous increase in AI patenting activities since 2013, with a significant boom in 2015-2016.geographical mapping using patent data (No.433).GLO Discussion Paper.

Fig
Fig S7.1A | Evolution of the AI research enterprise in the life sciences.Yearly counts of articles recorded in PubMed (n = 374,501) with AI-related keywords in titles or abstracts from 2000 to 2022.

Fig S7. 1B |
Fig S7.1B | Evolution of the AI research enterprise in the life sciences.Yearly counts of articles (n = 23,466) published in conference proceedings and recorded in OpenAlex with AI-related keywords in titles or abstracts from 2000 to 2022.

Fig S7. 2B |
Fig S7.2B | Geography of the AI life science research enterprise in terms of productivity.Counts of AI-focused life science articles published in conference proceedings and recorded in OpenAlex by country, cumulated for the years 2000 to 2022 (n = 23,466).

Fig S7 .
Fig S7.4B | Clinical AI research across countries.The share of a country's clinical research relative to global clinical research production (primary y-axis) and relative to all publications within the same country (secondary yaxis) for the 30 most productive countries in terms of clinical articles published in conference proceedings and recorded in OpenAlex (n =1,543).

S7. 5A .
Fig S7.5A | Geography of the AI life science research enterprise in terms of quality-adjusted productivity.Percentage shares of AI-focused life science articles published in high-ranked outlets recorded in PubMed by country, cumulated for the years 2000 to 2022 (n = 372,822).The analysis is limited to countries with at least 100 publications.

Fig S7. 5B |
Fig S7.5B | Geography of the AI life science research enterprise in terms of quality-adjusted productivity.Percentage shares of AI-focused life science articles published in high-ranked conference proceedings recorded in OpenAlex by country, cumulated for the years 2000 to 2022 (n = 21,491).The analysis is limited to countries with at least 100 publications.

Fig6|
Fig6 | Geography of the AI life science research enterprise in terms of quality-adjusted productivity.Percentage shares of AI life science articles published in high-ranked conference proceedings recorded in OpenAlex by geographic region and per year (n = 23,466).

Fig
Fig S7.8B.| The effect of international collaboration on scientific and clinical relevance (A); share of international collaborations over time (B); share of international collaborations by region (C).Incidence rate ratios (IRRs) obtained from negative binomial regressions of citations (n= 23,435) and clinical citations (n= 22,132) on a dummy variable for international collaboration on articles published in conference proceedings and recorded in OpenAlex, accounting for country of lead author, team size, and publication year (A).Percentage share of articles published in conference proceedings and recorded in OpenAlex with at least two authors affiliated in different countries (n= 23,466) (B).Percentage share of articles published in conference proceedings and recorded in OpenAlex with at least two authors affiliated in different countries by geographic region (n= 23,466) (C).

Fig
Fig S9.A | Alluvial diagram of international collaborations.Number of dyadic collaborations between authorsfrom different countries, aggregated to the regional level, on articles published in conference proceedings and recorded in OpenAlex.Dyadic collaborations are counted as co-authorships between a publication's lead author (last author or first author otherwise) and any other author on the author byline that is from a different country.Only international dyads are considered (n = 3,966 dyads).
The paper constructs a theoretical framework that includes four proximity dimensions (technological, organizational, temporal, and network) to explore the key driving factors of open innovation diffusion in the context of a global industrial chain.-Itusesartificialintelligence for healthcare as an example to explore the key driving factors of open innovation diffusion in the context of a global industrial chain.-Artificialintelligencetechnologies,such as machine learning and natural language processing, can be used to analyze large amounts of medical data, identify patterns, and make predictions, leading to more accurate diagnoses and personalized treatment plans.-Theempiricalanalysis verifies that technological proximity plays the leading role in innovation diffusion, while organizational and temporal proximities play secondary roles.-Thepaper highlights the significance of open innovation diffusion in the patent system and emphasizes the potential support from the complex innovation network.It aims to guide policymakers in optimizing innovation management and policy implementation by understanding the evolution route of open innovation.

of AI-related Keywords used in Search Strategy action
literature, Wang et al 2020 (p.399) state, for example: "Numerous studies seem to confirm that machine curated results in MAG achieve reasonable if not greater accuracy over commercial data sets with considerable amount of human effort."We use these concepts for two different purposes.First, we use the level 0 concepts to identify life science relevant conference proceedings publications.Second, we use the level 1 concepts to create heatmaps depicting the life science content of articles in our sample.A list of all OpenAlex concepts and their tree position can be found here: recognition, human action recognition, activity recognition, human activity recognition, adaboost, adaptive boosting, adversarial network, generative adversarial network, ambient intelligence, ant colony, ant colony optimisation, artificial intelligence, human aware artificial intelligence, association rule, autoencoder, autonomic computing, autonomous vehicle, autonomous weapon, backpropagation, Bayesian learning, bayesian network, bee colony, artificial bee colony algorithm, blind signal separation, bootstrap aggregation, brain computer interface, brownboost, chatbot, classification tree, cluster analysis, cognitive automation, cognitive computing, cognitive insight system, cognitive modelling, collaborative filtering, collision avoidance, community detection, computational intelligence, computational pathology, computer vision, cyber physical system, data mining, decision tree, deep belief network, deep learning, dictionary learning, dimensionality reduction, dynamic time warping, emotion recognition, ensemble learning, evolutionary algorithm, differential evolution algorithm, multi-objective evolutionary algorithm, evolutionary computation, face recognition, facial expression recognition, factorisation machine, feature engineering, feature extraction, feature learning, feature selection, firefly algorithm, fuzzy c, fuzzy environment, fuzzy logic, fuzzy number, fuzzy set, intuitionistic fuzzy set, fuzzy system, t s fuzzy system, Takagi-Sugeno fuzzy systems, gaussian mixture model, gaussian process, genetic algorithm, genetic programming, gesture recognition, gradient boosting, gradient tree boosting, graphical model, gravitational search algorithm, hebbian learning, hierarchical clustering, high-dimensional data, high-dimensional feature, high-dimensional input, high-dimensional model, high- https://docs.google.com/spreadsheets/d/1LBFHjPt4rj_9r0t0TTAlT68NwOtNH8Z21lBMsJDMoZg/edit#gid=575855905.S5 List dimensional space, high-dimensional system, image classification, image processing, image recognition, image retrieval, image segmentation, independent component analysis, inductive monitoring, instance-based learning, intelligence augmentation, intelligent agent, intelligent software agent, intelligent classifier, intelligent geometric computing, intelligent infrastructure, Kernel learning, K-means, latent dirichlet allocation, latent semantic analysis, latent variable, layered control system, learning automata, link prediction, logitboost, long short term memory (LSTM), lpboost, machine intelligence, machine learning, extreme machine learning, machine translation, machine vision, madaboost, MapReduce, Markovian, hidden Markov Liu et al. yielded a precision of 94%.Although the approach proposed by Liu and colleagues is thus slightly more precise, we opt for the search strategy of Baruffaldi et al. for retrieving over twice as many relevant articles.